Upload app.py

app.py

@@ -1,154 +1,57 @@
-import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import requests
+from PIL import Image
+from io import BytesIO
+from dotenv import load_dotenv
+import os
+
+# Загрузка токена из .env файла
+load_dotenv()
+API_TOKEN = os.getenv("HF_API_TOKEN")
+
+def generate_image(prompt, model_name):
+    API_URL = f"https://api-inference.huggingface.co/models/{model_name}"
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+
+    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
+    if response.status_code == 200:
+        return Image.open(BytesIO(response.content))
+    else:
+        return f"Ошибка: {response.status_code}, {response.text}"
+
+models = {
+    "Stable Diffusion v1.5": "Yntec/stable-diffusion-v1-5",
+    "DreamBooth v2": "stabilityai/dreambooth-v2",
+    "DALL-E Mini": "dalle-mini/dalle-mini",
+    "DeepAI Generator": "deepai/image-gen",
+    "Realistic Vision": "realistic-vision/image-gen",
+    "Artistic AI": "artistic-ai/v2"
+}
+
+def handle_input(prompt):
+    outputs = {}
+    for name, model in models.items():
+        outputs[name] = generate_image(prompt, model)
+    return list(outputs.values())
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Демо-генерация изображений с помощью различных моделей нейросетей")
+    with gr.Row():
+        user_input = gr.Textbox(label="Введите описание изображения",
+                                placeholder="Например, 'Астронавт верхом на лошади'")
+    with gr.Row():
+        outputs = [
+            gr.Image(label=name, interactive=False)
+            for name in models.keys()
+        ]
+    generate_button = gr.Button("Сгенерировать")
+
+    # Обработка клика
+    generate_button.click(
+        fn=handle_input,
+        inputs=[user_input],
+        outputs=outputs
+    )
+
+demo.launch()